1) Role Summary

The Principal Data Platform Engineer is a senior individual contributor who designs, evolves, and operationalizes the enterprise data platform that enables reliable, secure, and scalable analytics, ML/AI, and data-driven product capabilities. This role sets technical direction for data infrastructure, establishes engineering standards, and solves the highest-complexity platform problems spanning ingestion, storage, processing, governance, and serving.

This role exists in software and IT organizations because modern products, internal operations, and decision-making increasingly depend on high-quality, trusted, well-governed data delivered at scale with strong reliability and cost efficiency. The Principal Data Platform Engineer creates business value by reducing time-to-data, improving platform reliability and performance, enabling self-service analytics and ML, and lowering total cost of ownership through platform standardization and automation.

Role horizon: Current (with ongoing evolution toward more automated, policy-driven, and AI-augmented data operations).

Typical teams/functions interacted with: – Data Engineering, Analytics Engineering, BI/Analytics, Data Science/ML Engineering – SRE/Infrastructure, Cloud Platform/DevOps, Security/AppSec, Identity & Access Management – Product Engineering teams (microservices, event producers/consumers) – Enterprise Architecture, Governance/Risk/Compliance, Privacy/Legal – Finance (FinOps), Procurement/Vendor Management – Product Management (data platform roadmap), Program/Delivery Management

2) Role Mission

Core mission:
Build and continuously improve a secure, resilient, self-service data platform that delivers trusted data products (datasets, metrics, features, and events) with predictable performance, observability, governance, and cost control.

Strategic importance to the company: – Enables reliable analytics and reporting for product and business decisions. – Powers ML/AI model training and feature delivery. – Supports regulatory compliance (privacy, retention, auditability) where applicable. – Creates engineering leverage by standardizing patterns, tooling, and controls across data workflows.

Primary business outcomes expected: – Reduced time from data generation to consumption (time-to-insight / time-to-feature). – Improved data reliability (fewer incidents, faster recovery, consistent SLAs). – Lower unit cost per query / per TB processed / per pipeline run through architectural and operational improvements. – Increased adoption of governed self-service data capabilities by downstream teams. – Demonstrable controls for security, privacy, lineage, retention, and access management.

3) Core Responsibilities

Strategic responsibilities

1. Define data platform reference architecture across ingestion, storage, processing, orchestration, governance, and serving; align with enterprise architecture principles and product strategy.
2. Establish platform engineering standards (golden paths, templates, opinionated frameworks) that accelerate delivery while improving reliability and security.
3. Create and own multi-quarter roadmap inputs for platform modernization (e.g., lakehouse adoption, streaming maturity, metadata-driven governance, cost optimization).
4. Design platform capabilities for self-service (provisioning, access patterns, standardized datasets/metrics) to reduce bespoke engineering and improve scalability.
5. Drive platform build-vs-buy decisions by evaluating managed services and vendors; create objective selection criteria and migration plans.

Operational responsibilities

6. Ensure platform SLOs/SLAs for data freshness, availability, and performance; partner with SRE/Operations to implement reliability practices.
7. Lead incident response for major platform issues, including root cause analysis (RCA), corrective actions, and prevention via automation and guardrails.
8. Own capacity planning and cost management for data infrastructure (storage, compute, concurrency, streaming throughput); partner with FinOps.
9. Manage platform lifecycle operations: upgrades, patching strategy, deprecation plans, backward compatibility, and communication to users.
10. Implement operational observability for pipelines, jobs, clusters/warehouses, and data quality—ensuring actionable alerting, dashboards, and runbooks.

Technical responsibilities

11. Architect and implement ingestion patterns (batch, micro-batch, streaming, CDC) from operational systems, SaaS tools, logs, and product events.
12. Design scalable storage and compute patterns (lakehouse/warehouse, partitioning, file formats, caching, indexing, clustering) to meet performance and cost goals.
13. Build robust orchestration and dependency management patterns (DAG design, backfills, retries, idempotency, scheduling strategy).
14. Implement data quality and contract testing (schema enforcement, anomaly detection, freshness checks) and integrate results into CI/CD and runtime gating.
15. Design secure access models (RBAC/ABAC, row/column-level security, tokenization where relevant) aligned with least privilege and audit needs.
16. Enable governed data serving: curated datasets, semantic layers/metrics, feature stores (if applicable), APIs, and standardized consumption interfaces.
17. Improve developer experience (DX) for data engineers/analysts through local dev patterns, environment parity, testing frameworks, and CI/CD pipelines.

Cross-functional or stakeholder responsibilities

18. Partner with product engineering to define event schemas, data contracts, and instrumentation standards; influence upstream changes to reduce downstream complexity.
19. Align with security, privacy, and compliance to implement controls for data classification, retention, consent, and auditability.
20. Consult and mentor delivery teams adopting platform patterns; review architecture and critical PRs; unblock complex cross-domain integration issues.

Governance, compliance, or quality responsibilities

21. Own platform governance mechanisms: metadata management, lineage, catalog standards, access request workflows, and stewardship operating practices.
22. Implement “policy as code” guardrails where feasible (data access, resource constraints, encryption, tagging, retention) to reduce manual control failures.
23. Ensure documentation quality: reference architectures, runbooks, onboarding guides, and decision records that are maintained and discoverable.

Leadership responsibilities (Principal-level, IC leadership)

24. Set technical direction and influence across multiple teams without formal authority; align stakeholders on tradeoffs, sequencing, and standards.
25. Coach senior engineers and tech leads on architecture, reliability, and data engineering best practices; raise overall engineering maturity.
26. Lead cross-team technical programs (e.g., warehouse migration, streaming platform rollout, metadata platform adoption) through design reviews and phased execution.

4) Day-to-Day Activities

Daily activities

• Review platform health dashboards (pipeline success, lag, query latency, warehouse concurrency, streaming consumer lag).
• Triage platform support requests: access issues, performance regressions, schema changes, pipeline failures.
• Provide architectural guidance via design reviews and PR reviews for platform-critical changes.
• Work on one or two high-leverage technical threads (e.g., optimizing a core dataset pipeline, improving cluster autoscaling, implementing new governance controls).
• Communicate decisions and updates in engineering channels; clarify standards and recommended patterns.

Weekly activities

• Lead or participate in platform engineering standups and planning (priorities, risk review, dependency management).
• Conduct incident postmortems or operational reviews (recurring failures, noisy alerts, reliability trends).
• Meet with key stakeholder groups (Analytics, Data Science, Product Engineering) to validate platform roadmap needs.
• Review cost reports with FinOps (top cost drivers, query hotspots, storage growth, reserved capacity utilization).
• Run architecture office hours for teams onboarding to platform patterns.

Monthly or quarterly activities

• Quarterly roadmap planning and prioritization for platform capabilities; define measurable OKRs and SLO improvements.
• Platform maturity assessment (reliability, security controls, governance coverage, adoption metrics).
• Capacity planning and forecasting (storage, compute, network throughput, streaming partitions).
• Vendor/product reviews and renewal inputs; assess performance of managed services and contractual SLAs.
• Disaster recovery (DR) and business continuity testing for critical data services (context-specific but common in enterprise environments).

Recurring meetings or rituals

• Architecture Review Board (ARB) or equivalent technical governance forum (weekly/biweekly).
• Data Governance Council participation (monthly), focusing on metadata, access, and policy enforcement.
• Reliability review with SRE/Operations (weekly/biweekly): SLOs, error budgets, incident patterns.
• Security review checkpoints for major platform changes (as needed).
• Cross-functional schema/data contract review with product teams (weekly/biweekly in event-driven orgs).

Incident, escalation, or emergency work (if relevant)

• Serve as an escalation point for:
• Platform-wide outages or severe performance degradation.
• Widespread data quality issues impacting executive reporting or customer-facing features.
• Security incidents involving data access anomalies.
• During incidents:
• Coordinate technical response, isolate blast radius, restore service, communicate status.
• Ensure operational logging and evidence capture (especially for regulated contexts).
• Drive post-incident learning: systemic fixes, automation, and updated runbooks.

5) Key Deliverables

• Data platform reference architecture (current-state and target-state diagrams, standards, and integration patterns).
• Platform roadmap and capability backlog (quarterly plan, dependencies, success metrics).
• Golden path templates for pipelines, streaming consumers, CDC ingestion, and dataset publishing.
• IaC modules for repeatable provisioning (warehouses/clusters, storage, networking, IAM roles/policies).
• CI/CD pipelines for data workloads (build/test/deploy, environment promotion, rollback mechanisms).
• Data quality framework (tests, thresholds, anomaly detection, gating behavior, reporting).
• Observability suite: dashboards, alerting rules, SLO definitions, runbooks, on-call playbooks.
• Data governance artifacts: classification/tagging standards, access control patterns, retention policies, lineage coverage plans.
• Performance and cost optimization plan with measurable targets (query tuning, partitioning strategy, caching, workload isolation).
• Migration plans for major platform transitions (e.g., on-prem to cloud, Hadoop to lakehouse, warehouse consolidation).
• Technical decision records (ADRs) documenting key tradeoffs and rationale.
• Training materials: onboarding guides, brown-bag sessions, internal workshops for platform adoption.
• Executive-ready status reporting for major initiatives (progress, risks, cost trends, reliability trends).

6) Goals, Objectives, and Milestones

30-day goals (orientation and baseline)

• Map the current platform landscape: ingestion sources, storage layers, orchestration, serving patterns, and governance tooling.
• Review existing SLOs/SLAs (if any) and the top operational pain points (incidents, data quality failures, performance bottlenecks).
• Identify top 10 critical datasets/pipelines and their business owners; understand downstream dependencies and “mission critical” reporting.
• Establish working relationships with key stakeholders (Data Engineering leads, Analytics leadership, SRE, Security).
• Produce an initial platform risk and opportunity assessment (reliability, security gaps, cost hotspots, technical debt).

60-day goals (quick wins and stabilization)

• Deliver 2–3 high-impact improvements such as:
• Reduction in recurring pipeline failures through improved retry/idempotency patterns.
• Improved observability with standardized dashboards/alerts for critical workflows.
• A first “golden path” pipeline template with CI testing and quality checks.
• Propose updated platform SLOs and error budgets (availability, freshness, latency) and align stakeholders.
• Establish a platform intake and prioritization mechanism (support queue, ADR process, architecture review cadence).
• Create a cost baseline: identify cost drivers and propose first optimization actions.

90-day goals (direction-setting and adoption)

• Publish a target-state reference architecture and standards for new development (batch/streaming, storage formats, naming conventions, security controls).
• Implement at least one end-to-end exemplar (“lighthouse”) data product using recommended patterns (ingestion → processing → quality → serving).
• Formalize governance integration: catalog/lineage expectations, data classification tags, access workflows, and audit logging.
• Reduce MTTR and incident recurrence for top platform issues through automation and runbook improvements.
• Align with product engineering on event/data contract standards (schemas, versioning, compatibility rules).

6-month milestones (platform leverage)

• Achieve measurable improvement on 2–3 key platform outcomes, such as:
• 30–50% reduction in failed pipeline runs for critical workflows.
• 20–30% improvement in data freshness for prioritized domains.
• 10–20% cost reduction or cost avoidance through compute/storage optimization.
• Expand golden paths/templates to cover the majority of new pipeline development.
• Increase catalog/lineage coverage for priority data assets (e.g., 70–90% of Tier-1 datasets).
• Establish a reliable promotion model across environments (dev/test/prod) for data pipelines with automated testing.
• Implement workload isolation patterns (separate compute for ELT, BI, ML; streaming vs batch) to reduce contention.

12-month objectives (strategic outcomes)

• Enable self-service provisioning and publishing for data products with guardrails (reduced dependency on central platform team).
• Mature reliability practices: SLOs implemented, regular reliability reviews, measurable reduction in incident severity.
• Implement policy-driven governance (automated access controls, tagging enforcement, retention automation) to reduce manual compliance risk.
• Achieve high stakeholder satisfaction (analytics, DS, product engineering) measured through adoption and survey metrics.
• Deliver a modernization or migration program (context-specific), such as lakehouse consolidation or streaming maturity uplift.

Long-term impact goals (2+ years, role-dependent)

• Create a platform that supports near-real-time analytics and feature delivery where needed, without compromising governance or cost.
• Establish a scalable operating model: clear ownership boundaries, platform-as-a-product practices, and an internal community of practice.
• Reduce time-to-onboard for new data domains and teams from weeks to days via standardized tooling and automation.

Role success definition

The role is successful when the data platform is trusted, observable, secure, cost-efficient, and easy to adopt, with clear standards that scale across teams. Business stakeholders consistently get the data they need on time, and engineering teams can deliver data products with predictable quality and minimal bespoke infrastructure work.

What high performance looks like

• Proactively identifies systemic issues and solves them at the platform level (not via one-off fixes).
• Influences multiple teams to align on standards and governance with minimal friction.
• Demonstrates measurable improvements in SLOs, cost efficiency, and adoption.
• Produces clear technical artifacts (architecture, ADRs, runbooks) that enable faster delivery by others.
• Maintains a strong “security and privacy by design” posture without blocking velocity.

7) KPIs and Productivity Metrics

The Principal Data Platform Engineer is best measured through a mix of platform outcomes (reliability, adoption, cost), quality and governance coverage, and delivery effectiveness. Targets vary by company maturity and baseline; example benchmarks assume a mid-to-large cloud data platform.

KPI framework

Metric name	Type	What it measures	Why it matters	Example target / benchmark	Frequency
Tier-1 data availability	Reliability	% time critical datasets/serving endpoints meet availability SLO	Protects decision-making and data-driven product features	≥ 99.9% for Tier-1 pipelines/serving	Weekly/monthly
Data freshness SLO attainment	Outcome/Reliability	% of runs meeting freshness/latency targets (e.g., < X minutes/hours)	Direct proxy for time-to-insight/time-to-feature	≥ 95% of Tier-1 datasets meet freshness SLO	Weekly
MTTR for platform incidents	Reliability/Efficiency	Time from detection to restoration for P1/P2 incidents	Measures operational excellence and runbook effectiveness	P1: < 60–120 min; P2: < 4–8 hrs (context-specific)	Monthly
Incident recurrence rate	Reliability/Quality	% of incidents repeated within 30/60 days	Indicates whether fixes are systemic	< 10–15% recurrence	Monthly
Pipeline success rate (critical)	Quality/Reliability	% successful runs for Tier-1 pipelines	Reduces downstream disruption and manual intervention	≥ 99% successful scheduled runs	Weekly
Data quality test pass rate	Quality	% of defined checks passing for Tier-1 datasets	Directly reduces bad decisions and model errors	≥ 98–99% pass rate; rapid triage for failures	Daily/weekly
Data quality “time to detection”	Quality/Operational	Time from defect introduction to alert	Limits blast radius and rework	< 30–60 min for Tier-1	Weekly
Data quality “time to resolution”	Quality/Efficiency	Time from detection to fix/mitigation	Measures responsiveness and process maturity	Within SLO (e.g., < 1 business day for Tier-1)	Weekly/monthly
Query performance p95	Efficiency/Outcome	p95 latency for common BI/semantic queries	Improves user experience and adoption	Reduce p95 by 20% for top dashboards	Monthly
Cost per TB processed	Efficiency/Financial	Compute cost normalized by workload volume	Enables scaling with predictable spend	10–20% reduction QoQ (early) then steady	Monthly
Cost per active consumer	Efficiency/Financial	Spend relative to number of users/teams	Tracks platform leverage and unit economics	Improving trend (context-specific)	Quarterly
FinOps tagging/chargeback coverage	Governance/Efficiency	% workloads/costs properly tagged to owners	Enables accountability and optimization	≥ 95% resources tagged	Monthly
Catalog coverage (Tier-1)	Governance/Quality	% Tier-1 datasets registered with metadata	Enables discovery, governance, auditability	≥ 90–100% of Tier-1	Monthly
Lineage coverage (Tier-1)	Governance/Quality	% Tier-1 datasets with end-to-end lineage	Improves impact analysis and incident triage	≥ 80–90% Tier-1 lineage	Quarterly
Access request cycle time	Efficiency/Stakeholder	Time to provision approved access	Measures self-service maturity	< 1 day (or automated) for standard access	Monthly
Adoption of golden paths	Collaboration/Outcome	% new pipelines using templates/standards	Indicates platform scaling and reduced bespoke work	≥ 70–80% new builds use golden paths	Quarterly
Developer experience (DX) score	Stakeholder	Survey-based satisfaction of data builders	Predicts velocity and retention	≥ 4.2/5 (or +0.5 improvement)	Quarterly
Stakeholder NPS (analytics/DS)	Stakeholder/Outcome	Willingness to recommend platform internally	Measures trust and usability	Positive NPS; improving trend	Quarterly
Cross-team architecture review throughput	Output/Collaboration	Number of meaningful reviews completed with clear decisions	Ensures governance without bottlenecks	Context-specific; e.g., 10–20/month	Monthly
Mentorship / enablement sessions	Leadership	Office hours, trainings, guild participation	Scales knowledge and standards	2–4 sessions/month	Monthly

Notes on measurement: – Benchmarks must be calibrated to the organization’s baseline maturity. – KPIs should be paired with error budgets and clear definitions (what counts as availability, what qualifies as “Tier-1,” etc.). – Avoid vanity metrics (e.g., number of pipelines created) unless tied to outcomes.

8) Technical Skills Required

Must-have technical skills

1. Cloud data platform architecture (AWS/Azure/GCP)
   – Description: Designing data platforms using cloud-native services and patterns (networking, IAM, storage, compute).
   – Use: Selecting and integrating storage/compute/orchestration; ensuring reliability and security.
   – Importance: Critical

2. Data warehousing / lakehouse design
   – Description: Strong grasp of warehouse and lakehouse architectures, data modeling tradeoffs, and performance optimization.
   – Use: Designing curated layers, optimizing queries, partitioning, file formats, workload isolation.
   – Importance: Critical

3. Distributed processing (Spark or equivalent)
   – Description: Deep knowledge of distributed compute behavior, tuning, and failure handling.
   – Use: Building performant ETL/ELT, large-scale transformations, backfills, streaming processing.
   – Importance: Critical

4. SQL mastery (analytics-grade)
   – Description: Advanced SQL for transformations, performance, and governance (row/column security patterns vary by platform).
   – Use: Curated datasets, semantic models, query tuning, data validation.
   – Importance: Critical

5. Data orchestration and workflow engineering
   – Description: Designing resilient workflows with retries, idempotency, dependency management, and backfill strategies.
   – Use: Operating production pipelines and preventing cascading failures.
   – Importance: Critical

6. Infrastructure as Code (Terraform or equivalent)
   – Description: Declarative infrastructure provisioning and lifecycle management.
   – Use: Standardizing environments, enabling repeatable deployments, auditability.
   – Importance: Critical

7. Observability for data systems
   – Description: Metrics/logs/traces mindset applied to data pipelines and platform services.
   – Use: Dashboards, alerting, SLOs, root cause analysis.
   – Importance: Critical

8. Security fundamentals for data platforms
   – Description: IAM, encryption, key management, network controls, audit logging.
   – Use: Designing least-privilege access, secure data sharing, compliance alignment.
   – Importance: Critical

9. Version control and CI/CD for data workloads
   – Description: Git-based workflows, code review standards, automated testing/deployment.
   – Use: Reliable releases of pipelines and platform components.
   – Importance: Important to Critical (depends on maturity)

10. Programming in Python (and/or Scala/Java)
    – Description: Building platform utilities, pipeline code, automation, integration services.
    – Use: Framework development, custom connectors, data quality tooling, APIs.
    – Importance: Important

Good-to-have technical skills

1. Streaming platforms (Kafka/Kinesis/Pub/Sub) and stream processing
   – Use: Near-real-time pipelines, event-driven architectures, CDC streaming.
   – Importance: Important (Critical in event-heavy product orgs)

2. CDC and data replication tooling (Debezium/Fivetran/Database-native CDC)
   – Use: Reliable ingestion from OLTP systems; reducing batch brittleness.
   – Importance: Important

3. Data governance tooling (catalog, lineage, policy enforcement)
   – Use: Metadata management, discovery, auditability, stewardship workflows.
   – Importance: Important

4. Containerization and orchestration (Docker/Kubernetes)
   – Use: Running custom services, connectors, job runners, platform components.
   – Importance: Optional to Important (context-specific)

5. Data modeling patterns (dimensional, Data Vault, domain-oriented models)
   – Use: Curated analytical layers, scalable domain data products.
   – Importance: Important

6. Semantic layer / metrics store concepts
   – Use: Consistent KPI definitions, self-service BI, metric governance.
   – Importance: Important (varies by BI strategy)

7. Feature store patterns (online/offline)
   – Use: ML feature reuse, consistent training/serving features.
   – Importance: Optional to Important (ML maturity dependent)

Advanced or expert-level technical skills

1. Multi-tenant platform design and workload isolation
   – Description: Designing compute separation, concurrency management, quota enforcement, and noisy neighbor controls.
   – Use: Scaling platform across many teams with predictable performance.
   – Importance: Critical at Principal level

2. Performance engineering and cost optimization at scale
   – Description: Query tuning, file sizing, clustering/indexing, caching, autoscaling, reserved capacity strategy.
   – Use: Lowering spend while improving latency and throughput.
   – Importance: Critical

3. Data reliability engineering (DRE) practices
   – Description: SLOs, error budgets, incident command patterns for data, and reliability automation.
   – Use: Reducing business impact from data issues.
   – Importance: Critical

4. Data security architecture and privacy-by-design
   – Description: Policy design, tokenization, pseudonymization, consent/retention enforcement, audit controls.
   – Use: Minimizing regulatory and reputational risk.
   – Importance: Important to Critical (regulated environments)

5. Platform product management mindset (platform-as-a-product)
   – Description: Defining user journeys, measuring adoption, managing roadmaps and lifecycle.
   – Use: Ensuring platform investments translate to real usage and value.
   – Importance: Important

6. Complex migration engineering
   – Description: Incremental migration, dual-running, reconciliation, cutover strategy, deprecation.
   – Use: Platform transitions with minimal downtime and data inconsistency.
   – Importance: Important to Critical (during migrations)

Emerging future skills for this role (next 2–5 years)

1. Policy-as-code and automated governance at scale
   – Use: Automated enforcement of classification, access, retention, and residency constraints.
   – Importance: Important (growing)

2. AI-assisted data operations (AIOps for data)
   – Use: Anomaly detection, incident summarization, automated RCA suggestions, intelligent alert routing.
   – Importance: Important (emerging)

3. Data contract standardization and schema governance automation
   – Use: Continuous compatibility checks, producer accountability, reduced breakages.
   – Importance: Important

4. LLM-ready data architecture (vector search integration, unstructured data governance)
   – Use: Building pipelines for documents, embeddings, and retrieval systems with governance.
   – Importance: Optional to Important (context-specific)

9) Soft Skills and Behavioral Capabilities

1. Systems thinking and architectural judgment
   – Why it matters: Platform decisions create long-lived constraints and compounding effects on cost, reliability, and velocity.
   – On the job: Evaluates end-to-end workflows, identifies bottlenecks, anticipates second-order impacts.
   – Strong performance: Produces designs that reduce complexity, scale across teams, and remain adaptable.

2. Influence without authority (Principal-level leadership)
   – Why it matters: The role must align multiple teams to standards and migration paths.
   – On the job: Facilitates decisions, resolves disagreements, builds coalitions, earns trust through expertise and pragmatism.
   – Strong performance: Achieves adoption of platform patterns and governance without excessive escalation.

3. Technical communication (written and verbal)
   – Why it matters: Architecture, incidents, and governance require crisp, auditable communication.
   – On the job: Writes ADRs, runbooks, postmortems; explains tradeoffs to executives and engineers.
   – Strong performance: Produces clear artifacts that reduce ambiguity and accelerate implementation by others.

4. Operational ownership and calm under pressure
   – Why it matters: Data platforms are business-critical; incidents are inevitable.
   – On the job: Leads troubleshooting, prioritizes restoration, avoids thrash, coordinates responders.
   – Strong performance: Drives swift recovery and durable fixes; improves the system after incidents.

5. Pragmatic risk management
   – Why it matters: Data systems carry security, privacy, and financial risks; perfection can stall delivery.
   – On the job: Distinguishes acceptable risk from unacceptable risk; proposes mitigations and phased delivery.
   – Strong performance: Makes risk visible and actionable; improves controls without paralyzing teams.

6. Customer mindset (internal platform users)
   – Why it matters: A platform that isn’t usable will be bypassed, creating fragmentation and risk.
   – On the job: Runs office hours, collects feedback, improves DX and documentation, measures adoption.
   – Strong performance: Users prefer the platform’s golden paths because they are faster and safer.

7. Mentorship and talent scaling
   – Why it matters: Platform leverage comes from raising the baseline across teams.
   – On the job: Coaches senior engineers, reviews designs, teaches reliability and governance patterns.
   – Strong performance: Others independently apply standards; fewer repeated mistakes.

8. Conflict resolution and facilitation
   – Why it matters: Data ownership, definitions, and access can be politically charged.
   – On the job: Facilitates metric definition alignment, resolves ownership boundaries, negotiates SLAs.
   – Strong performance: Decisions stick; stakeholders feel heard; outcomes improve.

10) Tools, Platforms, and Software

Tooling varies by cloud and enterprise standards. The table below reflects common enterprise choices.

Category	Tool, platform, or software	Primary use	Common / Optional / Context-specific
Cloud platforms	AWS / Azure / GCP	Core infrastructure, managed data services	Common
Data warehousing	Snowflake / BigQuery / Azure Synapse / Redshift	Analytical storage/compute, BI workloads	Common (choice varies)
Lakehouse / storage	Databricks / Delta Lake / Apache Iceberg / Apache Hudi	Lakehouse tables, ACID, scalable storage	Common to Context-specific
Object storage	S3 / ADLS / GCS	Data lake storage, staging, logs	Common
Distributed compute	Apache Spark (Databricks/Synapse/EMR)	ETL/ELT, large-scale processing	Common
Streaming / messaging	Kafka / Confluent / Kinesis / Pub/Sub / Event Hubs	Event ingestion, streaming pipelines	Common to Context-specific
Orchestration	Airflow / Dagster / Prefect / Azure Data Factory	Workflow scheduling and dependency mgmt	Common
Transformation (analytics engineering)	dbt	SQL transformations, testing, docs	Common to Optional
Data quality	Great Expectations / Soda / Deequ	Data tests, validation, quality reporting	Common to Optional
Observability	Datadog / Prometheus + Grafana / CloudWatch / Azure Monitor	Metrics, dashboards, alerting	Common
Logging	ELK/Elastic / OpenSearch / Cloud-native logging	Centralized logs for platform services	Common
Tracing	OpenTelemetry / Datadog APM	Service tracing for custom components	Optional to Context-specific
CI/CD	GitHub Actions / GitLab CI / Jenkins / Azure DevOps	Build/test/deploy automation	Common
Source control	GitHub / GitLab / Bitbucket	Version control and collaboration	Common
IaC	Terraform / Pulumi / CloudFormation / Bicep	Repeatable provisioning, drift control	Common
Secrets & keys	Vault / AWS KMS / Azure Key Vault / GCP KMS	Secret management, encryption keys	Common
Security posture	Wiz / Prisma Cloud (where used)	Cloud security monitoring	Optional
Identity & access	Okta / Azure AD / IAM	SSO, RBAC/ABAC foundations	Common
Data catalog	Collibra / Alation / DataHub / Purview	Discovery, metadata, lineage	Common to Context-specific
Lineage	OpenLineage / Marquez / built-in warehouse lineage	Lineage capture and visualization	Optional to Context-specific
Feature store	Feast / Databricks Feature Store	ML feature management	Context-specific
Container platform	Kubernetes / EKS / AKS / GKE	Run custom services/connectors	Optional to Context-specific
Service mgmt (ITSM)	ServiceNow / Jira Service Management	Incident/problem/change management	Context-specific (enterprise common)
Collaboration	Slack / Microsoft Teams	Coordination, incident comms	Common
Documentation	Confluence / Notion	Runbooks, architecture, guides	Common
Project/product mgmt	Jira / Azure Boards	Planning, delivery tracking	Common
IDE / notebooks	VS Code / IntelliJ / Databricks notebooks	Development and investigation	Common
Artifact registry	Artifactory / Nexus / GitHub Packages	Package and artifact storage	Optional
Data sharing	Secure data shares / APIs / reverse ETL tools	Sharing curated data to apps/tools	Context-specific

11) Typical Tech Stack / Environment

Infrastructure environment

• Predominantly cloud-hosted, often multi-account/subscription with shared network controls.
• Mix of managed services (warehouse/lakehouse) and custom workloads (connectors, ingestion services).
• Strong emphasis on IaC, tagging standards, and environment separation (dev/test/prod).

Application environment

• Product applications are typically microservices-based, producing events/logs and writing to OLTP databases.
• Data platform integrates with operational sources via CDC, event streams, and batch extracts.
• Custom platform services may exist (schema registry, data contract validation service, metadata collectors).

Data environment

• Hybrid of:
• Warehouse for BI/semantic models and interactive analytics.
• Lakehouse/lake for large-scale storage, ML training datasets, and flexible processing.
• Streaming for near-real-time use cases (fraud, personalization, operational metrics).
• Layered data architecture (common patterns):
• Raw/landing → bronze/silver/gold or staging → curated marts/semantic layer.
• Data quality and metadata management integrated into CI/CD and runtime checks.

Security environment

• Enterprise IAM with role-based access, sometimes attribute-based controls (ABAC).
• Encryption in transit and at rest, centralized key management.
• Audit logging and monitoring for data access.
• Data classification and retention controls (especially in regulated contexts).

Delivery model

• Platform team operates as a product team:
• Roadmap, backlog, release notes, adoption measurement.
• Support model with clear escalation paths.
• Development practices include:
• Code reviews, automated tests, CI/CD, IaC PR approvals.
• Change management varies: lightweight in product-led orgs; formal CAB in IT-heavy enterprises.

Agile/SDLC context

• Agile delivery (Scrum/Kanban) common; platform work often uses Kanban for operational flow plus quarterly planning.
• Reliability work is planned as first-class backlog items (error budget policy, toil reduction).

Scale or complexity context

• Medium-to-large data volumes (TBs to PBs), high concurrency on BI/warehouse, multiple business domains.
• Multi-team environment with varying maturity; platform must provide safe defaults and guardrails.

Team topology

• Principal Data Platform Engineer typically sits within a Data Platform or Data Infrastructure team in Data & Analytics.
• Common peers:
• Staff/Principal Data Engineers
• Analytics Engineering Lead
• Data Reliability Engineer / SRE
• Security architects (matrixed)
• Typical reporting line: reports to Director of Data Engineering or Head of Data Platform.

12) Stakeholders and Collaboration Map

Internal stakeholders

• Head/Director of Data Platform / Director of Data Engineering (manager): prioritization, roadmap alignment, staffing needs, executive communication.
• Data Engineering teams: adoption of platform patterns, shared ownership boundaries, pipeline reliability.
• Analytics Engineering / BI: semantic layer, KPI definitions, dashboard performance, data freshness.
• Data Science / ML Engineering: training data availability, feature pipelines, governance for sensitive attributes.
• Product Engineering: event instrumentation, schema evolution, upstream data contracts, operational source changes.
• SRE / Cloud Platform / DevOps: incident response, infrastructure reliability, observability standards, capacity planning.
• Security/AppSec/IAM: access controls, audit requirements, encryption, threat modeling.
• Governance, Privacy, Legal: data classification, retention, consent, compliance reporting.
• Finance/FinOps: cost allocation, optimization strategies, budget forecasting.
• Internal Audit (context-specific): evidence of controls and auditability.

External stakeholders (as applicable)

• Cloud providers and managed-service vendors (support escalations, roadmap alignment, contract SLAs).
• External auditors (regulated industries) for evidence and control validation.

Peer roles

• Principal/Staff Software Engineers (platform/infrastructure)
• Principal Data Engineer (domain pipelines)
• Enterprise/Data Architect
• Security Architect
• Data Product Manager / Platform Product Manager
• Engineering Manager / TPM (for large programs)

Upstream dependencies

• Event producers and application databases (quality of instrumentation and schema discipline).
• Identity provider and enterprise access workflows.
• Network/security controls and provisioning pipelines.
• Vendor platform availability and quota limits.

Downstream consumers

• BI tools and dashboards; executive reporting
• Data science notebooks and model pipelines
• Product features (recommendations, search ranking, personalization, experimentation)
• Operational analytics (support, fraud, monitoring)

Nature of collaboration

• Co-design: with product engineering for event schemas and with analytics for metric definitions.
• Enablement: publishing templates, office hours, and code examples to accelerate teams.
• Governance alignment: translating compliance requirements into implementable platform controls.
• Joint operations: with SRE/operations for incident management and reliability improvements.

Typical decision-making authority

• Owns technical recommendations and reference architectures for the data platform.
• Co-owns standards with platform leadership and architecture governance bodies.
• Influences product engineering instrumentation standards via agreed contracts and shared accountability.

Escalation points

• Major incidents: escalates to on-call incident commander / SRE leadership and Head of Data.
• Cross-team standards disputes: escalates to architecture review board or engineering leadership.
• Security/privacy conflicts: escalates to Security leadership and Data Governance Council.

13) Decision Rights and Scope of Authority

Decisions this role can make independently

• Low-to-medium risk platform implementation details within approved architecture:
• Pipeline template patterns (retries, idempotency, logging structure)
• Default observability metrics and alert thresholds (within SLO policy)
• Performance tuning techniques and optimization changes with rollback plans
• Non-breaking improvements to IaC modules and CI/CD workflows
• Technical guidance in reviews:
• Approving PRs and design approaches aligned to standards
• Recommending deprecations or improvements for non-critical components

Decisions requiring team approval (platform engineering group)

• Changes to shared libraries/templates that affect many teams.
• Modifications to SLO definitions and alerting policies (to avoid noise and misaligned incentives).
• Introduction of new core platform dependencies (e.g., new orchestration tool, new metadata store).
• Backward-incompatible changes that require coordinated migration.

Decisions requiring manager/director approval

• Roadmap commitments and prioritization tradeoffs impacting multiple quarters.
• Significant cost-impacting changes (e.g., warehouse resize strategy, reserved capacity commitments).
• Major migrations (warehouse/lakehouse changes, orchestration replacement).
• On-call and support model changes that affect staffing.

Decisions requiring executive/security/compliance approval (context-specific)

• Data residency strategy, cross-border data movement, and major privacy posture changes.
• Adoption of new vendors handling sensitive data; contract/security review sign-off.
• Changes to retention policies impacting legal hold or regulatory requirements.
• Budget approvals beyond team-level thresholds.

Budget, architecture, vendor, delivery, hiring, compliance authority

• Budget: Usually indirect influence; provides cost models and recommendations; approvals sit with leadership.
• Architecture: Strong authority for platform reference architecture; must align with enterprise architecture governance.
• Vendor: Leads technical evaluation; procurement decisions approved by leadership/procurement.
• Delivery: Leads technical program execution; may guide TPMs; does not typically “own” headcount.
• Hiring: Participates heavily in interviews; defines bar for senior engineers; may not be the hiring manager.
• Compliance: Implements controls; compliance interpretation owned by security/legal/governance teams.

14) Required Experience and Qualifications

Typical years of experience

• Common range: 10–15+ years in software/data engineering, with 5+ years designing and operating data platforms at scale.
• Equivalent experience may include platform/SRE engineering with substantial data platform scope.

Education expectations

• Bachelor’s in Computer Science, Engineering, or related field is common.
• Advanced degree is not required but can be beneficial for certain ML-heavy contexts.

Certifications (relevant but rarely required)

• Cloud certifications (Optional): AWS Solutions Architect Professional, Google Professional Data Engineer, Azure Solutions Architect Expert.
• Security certifications (Context-specific): CCSK, Security+ (less common at Principal), or internal security training.
• Data platform vendor certs (Optional): Databricks, Snowflake certifications.

Prior role backgrounds commonly seen

• Senior/Staff Data Engineer with platform ownership
• Staff/Principal Software Engineer in infrastructure/platform teams who moved into data
• Data Warehouse Architect / Data Infrastructure Engineer
• Data Platform SRE / Reliability Engineer for data systems

Domain knowledge expectations

• Broad cross-domain applicability; should understand:
• Event-driven and OLTP-to-analytics integration patterns
• Analytics consumption patterns and BI constraints
• ML pipeline needs (training datasets, feature consistency) at a conceptual level
• Regulated environments require familiarity with:
• PII handling, retention, auditability, and least-privilege access patterns

Leadership experience expectations (IC leadership)

• Proven ability to lead technical direction across multiple teams.
• Experience driving major migrations or platform programs.
• Demonstrated mentorship and standard-setting through influence.

15) Career Path and Progression

Common feeder roles into this role

• Staff Data Engineer (platform-focused)
• Senior Staff Data Engineer (in some orgs)
• Staff Software Engineer (platform/infrastructure)
• Lead Data Engineer (IC track) with strong architecture exposure
• Data Architect (hands-on) transitioning toward engineering execution

Next likely roles after this role

• Distinguished Engineer / Fellow (Data Platforms) (IC track, enterprise-wide scope)
• Director of Data Platform / Head of Data Engineering (management track)
• Principal Architect (Data & Analytics) (architecture governance focus)
• Platform Product Lead (Data Platform) (platform-as-a-product leadership)

Adjacent career paths

• Data Reliability Engineering (DRE) leadership
• Security architecture for data platforms
• ML platform engineering (feature stores, model ops platforms)
• Enterprise cloud platform engineering (broader infra scope)

Skills needed for promotion beyond Principal

• Organization-wide technical strategy and long-range planning (2–3 year horizon).
• Stronger business case development (cost models, ROI, risk quantification).
• Track record of multiple successful cross-org programs with durable adoption.
• Standardization across domains with minimal friction (high trust, high clarity).
• Strong governance leadership: aligning policy, engineering, and audit requirements.

How this role evolves over time

• Early: stabilize reliability, define reference architecture, deliver golden paths.
• Mid: scale adoption, reduce toil through automation, mature governance and self-service.
• Later: enable advanced capabilities (near-real-time, AI/LLM-ready data flows), improve unit economics, and influence enterprise architecture.

16) Risks, Challenges, and Failure Modes

Common role challenges

• Ambiguous ownership boundaries between platform, domain data teams, and product engineering.
• Competing priorities: feature delivery vs reliability, governance vs speed, cost vs performance.
• Tool sprawl and fragmentation from past choices; multiple ingestion/orchestration patterns in flight.
• Upstream data instability (schema changes, poorly defined events, missing instrumentation).
• Scaling governance without creating bottlenecks or manual approval queues.

Bottlenecks

• Platform engineer becomes a “human gateway” for provisioning, access, and troubleshooting.
• Over-centralization: domain teams cannot deliver without platform team involvement.
• Lack of clear tiering (Tier-1 vs Tier-3) leading to over-investment in low-value pipelines.
• Slow change management processes that block needed reliability/security improvements.

Anti-patterns

• Building bespoke pipelines for each team instead of standardized templates.
• Treating data quality as “monitoring only” without enforceable contracts and gating.
• Overusing “raw data availability” as success, while curated data remains unreliable or undefined.
• Relying on tribal knowledge (no runbooks/ADRs) and hero-based incident response.
• Cost optimization via blunt constraints (e.g., shutting down compute) without understanding workload patterns.

Common reasons for underperformance

• Strong technical skills but insufficient influence/communication to drive adoption.
• Designing ideal-state architecture without incremental migration strategy.
• Over-indexing on tools rather than user needs and operational realities.
• Inadequate operational ownership (ignoring on-call realities, missing SLO thinking).
• Weak security/governance integration leading to rework and stakeholder distrust.

Business risks if this role is ineffective

• Executive reporting errors, poor decisions, and loss of confidence in data.
• Increased incident frequency and longer outages affecting business operations and product features.
• Escalating cloud spend without understanding drivers; unpredictable costs.
• Compliance failures (improper access, retention violations) leading to legal and reputational harm.
• Slower product iteration due to unreliable experimentation/metrics and brittle pipelines.

17) Role Variants

This role is consistent across organizations, but scope and emphasis shift by context.

By company size

• Small/mid-size (pre-IPO or scale-up):
• More hands-on implementation; fewer specialized teams.
• Emphasis on building foundational platform quickly, with pragmatic governance.
• Likely to own more end-to-end (infra + pipelines + standards).
• Large enterprise:
• More stakeholder management, formal governance, and multi-platform integration.
• Stronger emphasis on compliance, auditability, and operating model boundaries.
• More time spent on architecture reviews, standards, and migration programs.

By industry

• General SaaS/software (common default):
• Strong focus on product analytics, experimentation, customer usage data, and operational metrics.
• Financial services/healthcare/public sector (regulated):
• Stronger requirements for privacy, retention, audit logging, data minimization, and residency.
• More formal approvals; heavier emphasis on security architecture and evidence.
• E-commerce/consumer:
• Higher event volume; streaming and near-real-time use cases more common.
• Strong emphasis on attribution, personalization features, and experimentation platforms.

By geography

• Mostly similar globally; differences arise in:
• Data residency and cross-border transfer constraints.
• Local regulatory frameworks affecting privacy and retention.
• The role should be explicit about data residency patterns if operating in multi-region regulatory contexts.

Product-led vs service-led company

• Product-led:
• Tight integration with product engineering; strong event instrumentation and metrics definitions.
• Data platform treated as internal product; adoption metrics and user experience are key.
• Service-led / IT organization:
• Greater emphasis on data integration across enterprise systems, SLAs, and ITSM processes.
• More formal change management and service catalogs.

Startup vs enterprise

• Startup:
• Minimal governance initially; principal engineer sets foundational patterns to avoid future rework.
• Speed is critical; architecture must be scalable but lightweight.
• Enterprise:
• Must navigate existing systems, procurement, governance councils, and legacy platforms.
• Migration and standardization are core parts of the job.

Regulated vs non-regulated

• Non-regulated:
• Security still critical, but governance may emphasize discoverability and access control over audit evidence.
• Regulated:
• Data classification, retention automation, audit trails, and access reviews are first-class deliverables.
• Closer partnership with privacy/legal/security; more formal documentation and controls testing.

18) AI / Automation Impact on the Role

Tasks that can be automated (now and near-term)

• Pipeline scaffolding and template generation (CI/CD, standard DAGs, testing harnesses).
• Automated documentation from metadata (catalog population, lineage extraction, schema diffs).
• Anomaly detection for data freshness/volume/distribution shifts using statistical/ML methods.
• Incident summarization and triage assistance (correlating alerts, log summaries, suggested runbooks).
• Query optimization suggestions (indexing/clustering recommendations, identifying expensive queries).
• Policy enforcement automation (tag enforcement, access checks, retention workflows).

Tasks that remain human-critical

• Architecture and tradeoff decisions (cost vs latency vs governance vs complexity).
• Operating model design (ownership boundaries, service levels, support processes).
• Stakeholder alignment on metric definitions, domain ownership, and migration sequencing.
• Risk acceptance decisions (what controls are required, when exceptions are allowed).
• High-stakes incident leadership where context, judgment, and coordination matter.

How AI changes the role over the next 2–5 years

• The Principal Data Platform Engineer will increasingly:
• Manage policy-driven, metadata-first platforms (governance integrated into pipelines and access).
• Implement AI-assisted observability: fewer manual dashboards, more intelligent alerting and root-cause correlation.
• Support unstructured and semi-structured data pipelines for LLM/RAG use cases with strong governance.
• Develop developer copilots and internal tooling that reduce toil for data builders (code generation, debugging support).
• Expectations shift from “build pipelines” to “build platforms that build pipelines,” including automated guardrails and standardized data products.

New expectations caused by AI, automation, or platform shifts

• Stronger focus on:
• Data provenance and lineage (for AI accountability and auditing).
• Data quality as enforceable contracts (to reduce model risk and hallucination amplification).
• Secure handling of sensitive data used in training or retrieval workflows.
• Cost controls as workloads diversify (embedding generation, vector search, experimentation at scale).

19) Hiring Evaluation Criteria

What to assess in interviews

1. Platform architecture depth – Can the candidate design an end-to-end data platform with clear tradeoffs? – Do they understand reliability, security, and cost implications?

2. Operational excellence and reliability mindset – Experience with SLOs, incident response, and reducing recurrence. – Ability to design for failure and operational simplicity.

3. Scale and performance engineering – Evidence of tuning warehouses/lakehouses and distributed jobs at meaningful scale. – Ability to reason about concurrency, partitioning, file sizing, and caching.

4. Governance and security – Practical implementation of least privilege, audit logging, classification, and retention. – Ability to partner with security/legal without creating delivery gridlock.

5. Influence and leadership (IC) – Ability to drive standards and adoption across teams. – Quality of written communication (ADRs, postmortems, proposals).

6. Pragmatism and delivery – Incremental migration strategy and ability to deliver value in phases. – Avoids “boil the ocean” programs.

Practical exercises or case studies (recommended)

1. Architecture case study (60–90 minutes) – Prompt: “Design a cloud data platform for a SaaS product with batch + streaming needs, governance requirements, and cost constraints.” – Evaluate: clarity of architecture, tradeoffs, SLOs, security model, migration approach.

2. Operational scenario (30–45 minutes) – Prompt: “A Tier-1 dashboard is wrong; freshness SLO is breached; pipeline shows partial success. Walk through incident handling and RCA.” – Evaluate: triage approach, communication, containment, prevention.

3. Performance and cost tuning exercise (take-home or live) – Provide a simplified schema and query patterns; ask for optimization plan. – Evaluate: ability to identify bottlenecks, propose changes, define measurable outcomes.

4. Governance design mini-case – Prompt: “Implement row-level security and auditability for PII while enabling self-service analytics.” – Evaluate: IAM patterns, policy enforcement, usability.

Strong candidate signals

• Has led or co-led a major platform migration with minimal downtime and clear measurement.
• Demonstrates SLO thinking and can articulate reliability as an engineering product.
• Provides concrete examples of cost savings and performance improvements with metrics.
• Can describe how they drove adoption (templates, guardrails, documentation, office hours).
• Communicates with clarity; writes structured designs and postmortems.

Weak candidate signals

• Talks only about tools, not outcomes (reliability, adoption, governance, cost).
• Lacks operational ownership experience (no on-call, no incident leadership).
• Cannot articulate security/access control patterns beyond basic RBAC.
• Overly rigid architecture proposals without incremental path or risk management.
• Little evidence of cross-team influence.

Red flags

• Blames stakeholders or upstream teams without proposing contract-based solutions.
• Proposes bypassing governance/security as a default to “move fast.”
• No experience operating what they build; avoids accountability for production issues.
• Repeatedly introduces bespoke solutions without standardization strategy.

Scorecard dimensions (structured evaluation)

Dimension	Weight (example)	What “meets bar” looks like
Architecture & systems design	25%	End-to-end design with clear tradeoffs, scalable patterns, and migration strategy
Reliability & operations	20%	SLOs, incident leadership, automation to reduce recurrence
Performance & cost engineering	15%	Concrete tuning approaches, unit economics mindset
Security & governance	15%	Practical least privilege, auditability, retention/classification integration
Coding & engineering craft	10%	Clean, testable code; CI/CD/IaC literacy
Influence & communication	15%	Clear writing, stakeholder alignment, standards adoption evidence

20) Final Role Scorecard Summary

Category	Executive summary
Role title	Principal Data Platform Engineer
Role purpose	Architect and lead the evolution of a secure, reliable, scalable, cost-efficient data platform enabling analytics, ML/AI, and data-driven products through self-service capabilities, governance, and operational excellence.
Top 10 responsibilities	1) Define reference architecture 2) Set engineering standards/golden paths 3) Ensure SLOs and operational reliability 4) Lead major incidents/RCA 5) Architect ingestion (batch/streaming/CDC) 6) Optimize storage/compute and query performance 7) Implement orchestration patterns and CI/CD 8) Establish data quality and contracts 9) Implement governance (catalog/lineage/access/retention) 10) Influence cross-team adoption and mentor engineers
Top 10 technical skills	Cloud architecture; warehouse/lakehouse design; Spark/distributed processing; advanced SQL; orchestration engineering; IaC (Terraform); observability/SLOs; data security/IAM; CI/CD and Git workflows; Python (plus Scala/Java optional)
Top 10 soft skills	Systems thinking; influence without authority; technical communication; operational ownership; pragmatic risk management; customer mindset; mentorship; facilitation/conflict resolution; prioritization judgment; cross-functional collaboration
Top tools/platforms	Cloud (AWS/Azure/GCP), Snowflake/BigQuery/Synapse/Redshift, Databricks/Delta/Iceberg, S3/ADLS/GCS, Airflow/Dagster/Prefect, Kafka/Kinesis/Pub/Sub, dbt (common), Terraform, Datadog/Grafana/CloudWatch, Collibra/Alation/DataHub/Purview
Top KPIs	Tier-1 availability; freshness SLO attainment; MTTR; incident recurrence; pipeline success rate; data quality pass rate; query p95 latency; cost per TB processed; catalog/lineage coverage; golden path adoption/DX score
Main deliverables	Reference architecture; roadmap; golden path templates; IaC modules; CI/CD pipelines; quality framework; observability dashboards/alerts/runbooks; governance controls (catalog/lineage/access/retention); optimization plans; migration plans; ADRs; enablement/training materials
Main goals	30/60/90-day stabilization + standards; 6-month measurable reliability/cost/freshness gains; 12-month self-service and policy-driven governance maturity; sustained adoption and stakeholder trust
Career progression options	Distinguished Engineer/Fellow (Data Platforms), Principal Architect (Data & Analytics), Director/Head of Data Platform (management), Data Reliability Engineering leadership, ML platform engineering (adjacent)